The Differentiable Neural Computer (DNC) is an architecture where external memory is attached to an Artificial Neural Network (ANN), akin to conventional computers where random-access memory is attached to a central processing unit [1]}. The external memory gives the DNC additional capacity, as the memory provides the ANN with the capability to track and manipulate the input data directly rather than through the ANN weights and neuron activity. The authors of the DNC show that it can learn to solve complex algorithmic tasks that are difficult for traditional ANNs – from sorting lists to graph traversal [2]}, [1]}. The authors train the DNC using curriculum learning across all tasks, and observe by inspecting its memory contents that it learns and improves algorithms over the curriculum steps.
